Results for “Image representation” — Page 13

NASA NTRS1991

Method for providing a polarization filter for processing synthetic aperture radar image data

A polarization filter can maximize the signal-to-noise ratio of a polarimetric SAR and help discriminate between targets or enhance image features, e.g., enhance contract between different types of target. The method disclosed is based on the Stokes matrix/Stokes vector representation, so the targets of interest can be extended targets, and the method can also be applied to the case of bistatic polarimetric radars.

Dubois, Pascale C.↗

NASA NTRS2008

SDO FlatSat Facility

The goal of the Solar Dynamics Observatory (SDO) is to understand and, ideally, predict the solar variations that influence life and society. It's instruments will measure the properties of the Sun and will take hifh definition images of the Sun every few seconds, all day every day. The FlatSat is a high fidelity electrical and functional representation of the SDO spacecraft bus. It is a high fidelity test bed for Integration & Test (I & T), flight software, and flight operations. For I & T purposes FlatSat will be a driver to development and dry run electrical integration procedures, STOL test procedures, page displays, and the command and telemetry database. FlatSat will also serve as a platform for flight software acceptance and systems testing for the flight software system component including the spacecraft main processors, power supply electronics, attitude control electronic, gimbal control electrons and the S-band communications card. FlatSat will also benefit the flight operations team through post-launch flight software code and table update development and verification and verification of new and updated flight operations products. This document highlights the benefits of FlatSat; describes the building of FlatSat; provides FlatSat facility requirements, access roles and responsibilities; and, and discusses FlatSat mechanical and electrical integration and functional testing.

Amason, David L.↗

NASA NTRS1998

Clementine High Resolution Camera Mosaicking Project: CL 6022; 80 deg S to 90 deg S Latitude, South Periapsis - Volume 22

This compact disk (CD) is part of the Clementine I high resolution (HiRes) camera lunar image mosaics developed by Malin Space Science Systems (MSSS). These mosaics were developed through calibration and semi-automated registration against the recently released geometrically and photometrically controlled Ultraviolet/Visible (UV/Vis) Basemap Mosaic, which is available through the PDS, as CD-ROM volumes CL_3001-3015. The HiRes mosaics are compiled from non-uniformity corrected, 750 nanometer ("D") filter high resolution observations from the HiRes imaging system onboard the Clementine Spacecraft. The geometric control is provided by the U.S. Geological Survey (USGS) Clementine Basemap Mosaic compiled from the 750 nm Ultraviolet/Visible Clementine imaging system. Calibration was achieved by removing the image nonuniformity largely caused by the HiRes system's light intensifier. Also provided are offset and scale factors, achieved by a fit of the HiRes data to the corresponding photometrically calibrated UV/Vis basemap that approximately transform the 8-bit HiRes data to photometric units. The mosaics on this CD are compiled from polar data (latitudes greater than 80 degrees), and are presented in the stereographic projection at a scale of 30 m/pixel at the pole, a resolution 5 times greater than that (150 m/pixel) of the corresponding UV/Vis polar basemap. This 5:1 scale ratio is in keeping with the sub-polar mosaic, in which the HiRes and UV/Vis mosaics had scales of 20 m/pixel and 100 m/pixel, respectively. The equal-area property of the stereographic projection made this preferable for the HiRes polar mosaic rather than the basemap's orthographic projection. Thus, a necessary first step in constructing the mosaic was the reprojection of the UV/Vis basemap to the stereographic projection. The HiRes polar data can be naturally grouped according to the orbital periapsis, which was in the south during the first half of the mapping mission and in the north during the second half. Images in each group have generally uniform intrinsic resolution, illumination, exposure and gain. Rather than mingle data from the two periapsis epochs, separate mosaics are provided for each, a total of 4 polar mosaics. The mosaics are divided into 100 square tiles of 2250 pixels (approximately 2.2 deg near the pole) on a side. Not all squares of this grid contain HiRes mosaic data, some inevitably since a square is not a perfect representation of a (latitude) circle, others due to the lack of HiRes data. This CD also contains ancillary data files that support the HiRes mosaic. These files include browse images with UV/Vis context stored in a Joint Photographic Experts Group (JPEG) format, index files ('imgindx.tab' and 'srcindx.tab') that tabulate the contents of the CD, and documentation files. For more information on the contents and organization of the CD volume set refer to the "FILES, DIRECTORIES AND DISK CONTENTS" section of this document. The image files are organized according to NASA's Planetary Data System (PDS) standards. An image file (tile) is organized as a PDS labeled file containing an "image object".

Malin, Michael↗

NASA NTRS1998

Clementine High Resolution Camera Mosaicking Project: CL 6020; 80 deg N to 90 deg N Latitude, South Periapsis - Volume 20

This compact disk (CD) is part of the Clementine I high resolution (HiRes) camera lunar image mosaics developed by Malin Space Science Systems (MSSS). These mosaics were developed through calibration and semi-automated registration against the recently released geometrically and photometrically controlled Ultraviolet/Visible (UV/Vis) Basemap Mosaic, which is available through the PDS, as CD-ROM volumes CL_3001-3015. The HiRes mosaics are compiled from non-uniformity corrected, 750 nanometer ("D") filter high resolution observations from the HiRes imaging system onboard the Clementine Spacecraft. The geometric control is provided by the U.S. Geological Survey (USGS) Clementine Basemap Mosaic compiled from the 750 nm Ultraviolet/Visible Clementine imaging system. Calibration was achieved by removing the image nonuniformity largely caused by the HiRes system's light intensifier. Also provided are offset and scale factors, achieved by a fit of the HiRes data to the corresponding photometrically calibrated UV/Vis basemap that approximately transform the 8-bit HiRes data to photometric units. The mosaics on this CD are compiled from polar data (latitudes greater than 80 degrees), and are presented in the stereographic projection at a scale of 30 m/pixel at the pole, a resolution 5 times greater than that (150 m/pixel) of the corresponding UV/Vis polar basemap. This 5:1 scale ratio is in keeping with the sub-polar mosaic, in which the HiRes and UV/Vis mosaics had scales of 20 m/pixel and 100 m/pixel, respectively. The equal-area property of the stereographic projection made this preferable for the HiRes polar mosaic rather than the basemap's orthographic projection. Thus, a necessary first step in constructing the mosaic was the reprojection of the UV/Vis basemap to the stereographic projection. The HiRes polar data can be naturally grouped according to the orbital periapsis, which was in the south during the first half of the mapping mission and in the north during the second half. Images in each group have generally uniform intrinsic resolution, illumination, exposure and gain. Rather than mingle data from the two periapsis epochs, separate mosaics are provided for each, a total of 4 polar mosaics. The mosaics are divided into 100 square tiles of 2250 pixels (approximately 2.2 deg near the pole) on a side. Not all squares of this grid contain HiRes mosaic data, some inevitably since a square is not a perfect representation of a (latitude) circle, others due to the lack of HiRes data. This CD also contains ancillary data files that support the HiRes mosaic. These files include browse images with UV/Vis context stored in a Joint Photographic Experts Group (JPEG) format, index files ('imgindx.tab' and 'srcindx.tab') that tabulate the contents of the CD, and documentation files. For more information on the contents and organization of the CD volume set refer to the "FILES, DIRECTORIES AND DISK CONTENTS" section of this document. The image files are organized according to NASA's Planetary Data System (PDS) standards. An image file (tile) is organized as a PDS labeled file containing an "image object".

Malin, Michael↗

NASA NTRS1998

Clementine High Resolution Camera Mosaicking Project: CL 6021; 80 deg S to 90 deg S Latitude, North Periapsis - Volume 21

This compact disk (CD) is part of the Clementine I high resolution (HiRes) camera lunar image mosaics developed by Malin Space Science Systems (MSSS). These mosaics were developed through calibration and semi-automated registration against the recently released geometrically and photometrically controlled Ultraviolet/Visible (UV/Vis) Basemap Mosaic, which is available through the PDS, as CD-ROM volumes CL_3001-3015. The HiRes mosaics are compiled from non-uniformity corrected, 750 nanometer ("D") filter high resolution observations from the HiRes imaging system onboard the Clementine Spacecraft. The geometric control is provided by the U. S. Geological Survey (USGS) Clementine Basemap Mosaic compiled from the 750 nm Ultraviolet/Visible Clementine imaging system. Calibration was achieved by removing the image nonuniformity largely caused by the HiRes system's light intensifier. Also provided are offset and scale factors, achieved by a fit of the HiRes data to the corresponding photometrically calibrated UV/Vis basemap that approximately transform the 8-bit HiRes data to photometric units. The mosaics on this CD are compiled from polar data (latitudes greater than 80 degrees), and are presented in the stereographic projection at a scale of 30 m/pixel at the pole, a resolution 5 times greater than that (150 m/pixel) of the corresponding UV/Vis polar basemap. This 5:1 scale ratio is in keeping with the sub-polar mosaic, in which the HiRes and UV/Vis mosaics had scales of 20 m/pixel and 100 m/pixel, respectively. The equal-area property of the stereographic projection made this preferable for the HiRes polar mosaic rather than the basemap's orthographic projection. Thus, a necessary first step in constructing the mosaic was the reprojection of the UV/Vis basemap to the stereographic projection. The HiRes polar data can be naturally grouped according to the orbital periapsis, which was in the south during the first half of the mapping mission and in the north during the second half. Images in each group have generally uniform intrinsic resolution, illumination, exposure and gain. Rather than mingle data from the two periapsis epochs, separate mosaics are provided for each, a total of 4 polar mosaics. The mosaics are divided into 100 square tiles of 2250 pixels (approximately 2.2 deg near the pole) on a side. Not all squares of this grid contain HiRes mosaic data, some inevitably since a square is not a perfect representation of a (latitude) circle, others due to the lack of HiRes data. This CD also contains ancillary data files that support the HiRes mosaic. These files include browse images with UV/Vis context stored in a Joint Photographic Experts Group (JPEG) format, index files ('imgindx.tab' and 'srcindx.tab') that tabulate the contents of the CD, and documentation files. For more information on the contents and organization of the CD volume set refer to the "FILES, DIRECTORIES AND DISK CONTENTS" section of this document. The image files are organized according to NASA's Planetary Data System (PDS) standards. An image file (tile) is organized as a PDS labeled file containing an "image object".

Malin, Michael↗

NASA NTRS1998

Clementine High Resolution Camera Mosaicking Project: CL 6019; 80 deg N to 90 deg N Latitude, North Periapsis - Volume 19

This compact disk (CD) is part of the Clementine I high resolution (HiRes) camera lunar image mosaics developed by Malin Space Science Systems (MSSS). These mosaics were developed through calibration and semi-automated registration against the recently released geometrically and photometrically controlled Ultraviolet/Visible (UV/Vis) Basemap Mosaic, which is available through the PDS, as CD-ROM volumes CL_3001-3015. The HiRes mosaics are compiled from non-uniformity corrected, 750 nanometer ("D") filter high resolution observations from the HiRes imaging system onboard the Clementine Spacecraft. The geometric control is provided by the U. S. Geological Survey (USGS) Clementine Basemap Mosaic compiled from the 750 nm Ultraviolet/Visible Clementine imaging system. Calibration was achieved by removing the image nonuniformity largely caused by the HiRes system's light intensifier. Also provided are offset and scale factors, achieved by a fit of the HiRes data to the corresponding photometrically calibrated UV/Vis basemap that approximately transform the 8-bit HiRes data to photometric units. The mosaics on this CD are compiled from polar data (latitudes greater than 80 degrees), and are presented in the stereographic projection at a scale of 30 m/pixel at the pole, a resolution 5 times greater than that (150 m/pixel) of the corresponding UV/Vis polar basemap. This 5:1 scale ratio is in keeping with the sub-polar mosaic, in which the HiRes and UV/Vis mosaics had scales of 20 m/pixel and 100 m/pixel, respectively. The equal-area property of the stereographic projection made this preferable for the HiRes polar mosaic rather than the basemap's orthographic projection. Thus, a necessary first step in constructing the mosaic was the reprojection of the UV/Vis basemap to the stereographic projection. The HiRes polar data can be naturally grouped according to the orbital periapsis, which was in the south during the first half of the mapping mission and in the north during the second half. Images in each group have generally uniform intrinsic resolution, illumination, exposure and gain. Rather than mingle data from the two periapsis epochs, separate mosaics are provided for each, a total of 4 polar mosaics. The mosaics are divided into 100 square tiles of 2250 pixels (approximately 2.2 deg near the pole) on a side. Not all squares of this grid contain HiRes mosaic data, some inevitably since a square is not a perfect representation of a (latitude) circle, others due to the lack of HiRes data. This CD also contains ancillary data files that support the HiRes mosaic. These files include browse images with UV/Vis context stored in a Joint Photographic Experts Group (JPEG) format, index files ('imgindx.tab' and 'srcindx.tab') that tabulate the contents of the CD, and documentation files. For more information on the contents and organization of the CD volume set refer to the "FILES, DIRECTORIES AND DISK CONTENTS" section of this document. The image files are organized according to NASA's Planetary Data System (PDS) standards. An image file (tile) is organized as a PDS labeled file containing an "image object".

Malin, Michael↗

NASA NTRS1985

Optimal frequency domain textural edge detection filter

An optimal frequency domain textural edge detection filter is developed and its performance evaluated. For the given model and filter bandwidth, the filter maximizes the amount of output image energy placed within a specified resolution interval centered on the textural edge. Filter derivation is based on relating textural edge detection to tonal edge detection via the complex low-pass equivalent representation of narrowband bandpass signals and systems. The filter is specified in terms of the prolate spheroidal wave functions translated in frequency. Performance is evaluated using the asymptotic approximation version of the filter. This evaluation demonstrates satisfactory filter performance for ideal and nonideal textures. In addition, the filter can be adjusted to detect textural edges in noisy images at the expense of edge resolution.

Townsend, J. K.↗

NASA NTRS1980

Optical communication with two-photon coherent states. III - Quantum measurements realizable with photoemissive detectors

Homodyne detection is shown to achieve the same signal-to-noise ratio as the quantum field quadrature measurement, thus providing a receiver which realizes linear modulation TCS performance gain. The full equivalence of homodyne detection and single-quadrature field measurement is established. A heterodyne configuration which uses a TCS image-band oscillator in addition to the usual coherent state local oscillator is studied. Results are obtained by means of a representation theorem which shows that photoemissive detection realizes the photon flux density measurement.

Yuen, H. P.↗

NASA NTRS1990

Method for providing a polarization filter for processing synthetic aperture radar image data

A polarization filter can maximize the signal-to-noise ratio of a polarimetric synthetic aperture radar (SAR) and help discriminate between targets or enhance image features, e.g., enhance contrast between different types of target. The method disclosed is based on the Stokes matrix/ Stokes vector representation, so the targets of interest can be extended targets, and the method can also be applied to the case of bistatic polarimetric radars.

Dubois, Pascale C.↗

NASA NTRS1992

Recognizing Patterns In Log-Polar Coordinates

Log-Hough transform is basis of improved method for recognition of patterns - particularly, straight lines - in noisy images. Takes advantage of rotational and scale invariance of mapping from Cartesian to log-polar coordinates, and offers economy of representation and computation. Unification of iconic and Hough domains simplifies computations in recognition and eliminates erroneous quantization of slopes attributable to finite spacing of Cartesian coordinate grid of classical Hough transform. Equally efficient recognizing curves. Log-Hough transform more amenable to massively parallel computing architectures than traditional Cartesian Hough transform. "In-place" nature makes it possible to apply local pixel-neighborhood processing.

Weiman, Carl F. R.↗

NASA NTRS1993

Learning fuzzy information in a hybrid connectionist, symbolic model

An instance-based learning system is presented. SC-net is a fuzzy hybrid connectionist, symbolic learning system. It remembers some examples and makes groups of examples into exemplars. All real-valued attributes are represented as fuzzy sets. The network representation and learning method is described. To illustrate this approach to learning in fuzzy domains, an example of segmenting magnetic resonance images of the brain is discussed. Clearly, the boundaries between human tissues are ill-defined or fuzzy. Example fuzzy rules for recognition are generated. Segmentations are presented that provide results that radiologists find useful.

Romaniuk, Steve G.↗

NASA NTRS2020

Reaching Broad Audiences from a Large Agency Setting

NASA’s missions, engineering accomplishments, and scientific findings have inspired generations and advanced our understanding of the world we live in. NASA’s Earth science data are acquired by various sources, including satellites, aircraft, and field measurements. Captured data, their by-products, and their visual representations developed by research teams become available within few hours after satellite overpass or processing through a variety of NASA’s imaging, mapping services, and portals. Such online services as the Global Imagery Browse Services (GIBS), Worldview, LANCE, and LAADS DAAC are freely and openly available thanks to NASA’s Earth-Observing Satellite Data and Information Systems (EOSDIS). These services provide access to products created over the last 30 years, support a broad range of users from the scientific community to the general public, and cover a multitude of applications such as basic and applied scientific research, natural hazard and disaster monitoring, and social and educational outreach. In order to illustrate the significance of the overall work, the visualization products, and the broad range of users, we present three case studies: NASA’s Black Marble Product Suite, the Global Imagery Browse Services (GIBS) and Worldview, and the scientific visualization production process to communicate results to the scientific community and the general public.

Helen-Nicole Kostis↗

NASA NTRS2024

A Multi‐Probe Automated Classification of Ice Crystal Habits During the IMPACTS Campaign

Although all ice crystals are unique, many can be grouped together by shape or habit, with members of a habit class sharing similar representations of properties such as fall velocity and growth rate. A decision tree algorithm designed to be adaptable to any particle imaging probe, thus enabling the creation of habit size distributions over a size range larger than that of any probe on its own, is used to classify ice crystals imaged by three airborne cloud probes in mid-latitude winter cyclones during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. Crystals are sorted into seven habit classes based on their morphological properties: sphere, column/needle, plate, graupel, dendrite, aggregate, and irregular. Although adaptability was its primary goal, the algorithm was found to be moderately skillful for identifying idealized habit images. Quantitative tests of the algorithm’s adaptability displayed mixed results, as Two-Dimensional Stereo Probe (2DS) classifications showed moderate correlation with Particle Habit Imaging and Polar Scattering Probe (PHIPS) classifications, but only weak correlation with High Volume Precipitation Spectrometer (HVPS) classifications. The algorithm was applied to random sets of images from each probe in a case study of a mesoscale snow band sampled on 7 February 2020. In the case study, qualitative analysis of particle images revealed general agreement on classifications among the probes, supporting the algorithm’s applicability to multiple cloud probes. Most classifications appeared correct upon manual inspection, suggesting that in practical use, the algorithm is reasonably able to classify non-idealized images.

Julian Schima↗

NASA NTRS2008

Matching Observations to Model Resolution for Future Weather and Climate Applications

High spatial resolution sounding observations will improve initialization and assimilation into the next generation forecast models and validation of the next generation of climate models. One such advanced sounder concept for low earth orbit is the Advanced Remote-sensing Imaging Emission Spectrometer (ARIES) which proposes to provide highvspatial hyperspectral resolution observations in the mid to longwave infrared. This paper explores the effects of spatial resolution on the errors expected from the combined use of models and observations for representing scene information. We calculate the frequency response of the instrument and model and determine the error at any given spatial frequency. The results show that it is vital to have observations match the spatial resolution of models to minimize the uncertainty in the representation of the scene contents.

spatial resolution↗

NASA NTRS1992

The spectral world coordinate systems in IRAF/NOAO

The world coordinate system (WCS) for dispersion calibrated spectra used in the IRAF/NOAO spectroscopy packages is described. In particular, the image header keywords which define the coordinates in an 'image' pixel array. These keywords appear both as part of the IRAF image structure and map directly to FITS format. The types of spectra include multidimensional images with spectral dispersion as one axis, one dimensional images, and a special 'multispec' format consisting of multiple, semi-independent, one dimensional spectra in two or three dimensional images. The types of coordinate systems include multidimensional linear coordinates with individual keywords and one dimensional linear and nonlinear coordinates for the 'multispec' images stored in IRAF WCS attributes. The nonlinear world coordinate systems include polynomial, spline, sampled table, and look-up table function representations.

Valdes, F.↗

NASA NTRS2009

NASA Tech Briefs, August 2009

Topics covered include: Aligning a Receiving Antenna Array to Reduce Interference; Collecting Ground Samples for Balloon-Borne Instruments; Tethered Pyrotechnic Apparatus for Acquiring a Ground Sample; Enhanced Video-Oculography System; Joint Carrier-Phase Synchronization and LDPC Decoding; Dual-Polarization, Sideband-Separating, Balanced Receiver for 1.5 THz Modular Battery Charge Controller; Efficient Multiplexer FPGA Block Structures Based on G4FETs; VLSI Microsystem for Rapid Bioinformatic Pattern Recognition; Low-Noise Amplifier for 100 to 180 GHz; Improved Fabrication of Ceramic Matrix Composite/Foam Core Integrated Structures; Inert Welding/Brazing Gas Filters and Dryers; Fabricating Copper Nanotubes by Electrodeposition; Reducing Aerodynamic Drag on Empty Open Cargo Vehicles; Rotary Percussive Auto-Gopher for Deep Drilling and Sampling; More About Reconfigurable Exploratory Robotic Vehicles; Thermostatic Valves Containing Silicone-Oil Actuators; Improving Heat Flux Performance of Flat Surface in Spray-Cooling Systems; Treating Fibrous Insulation to Reduce Thermal Conductivity; Silica-Aerogel Composites Opacified with La(sub0.7)Sr(sub0.3)MnO3; Cyclic Oxidation Behavior of CuCrAl Cold-Sprayed Coatings for Reusable Launch Vehicles; Ceramic Fiber Structures for Cryogenic Load-Bearing Applications; Elastomer Reinforced with Carbon Nanotubes; Biologically Inspired Purification and Dispersion of SWCNTs; A Technique for Adjusting Eigenfrequencies of WGM Resonators; Low-Pressure, Field-Ionizing Mass Spectrometer; Modifying Operating Cycles to Increase Stability in a LITS; Chamber for Simulating Martian and Terrestrial Environments; Algorithm for Detecting a Bright Spot in an Image; Extreme Programming: Maestro Style; Adaptive Behavior for Mobile Robots; Protocol for Communication Networking for Formation Flying; Planning Complex Sequences Using Compressed Representations; and Self-Supervised Learning of Terrain Traversability from Proprioceptive Sensors.

Source record↗

NASA NTRS2021

Precipitation Retrievals from Passive Microwave Cross-Track Sensors: The Precipitation Retrieval and Profiling Scheme

The retrieval of precipitation (snowfall and rainfall) from satellite sensors on a global basis is essential in aiding our knowledge and understanding of the Earth System and to many societal applications. Measurements from surface-based instruments are essentially limited to populated regions, necessitating the use of satellite-based observations to provide estimates of precipitation across the whole of the Earth’s surface. The temporal and spatial variability of precipitation requires adequate sampling, especially at finer resolutions. It is therefore necessary to exploit all available data from precipitation-capable satellites to ensure the proper representation of precipitation. To date, the estimation of precipitation using passive microwave observations has been largely concentrated upon the conically-scanning imaging instruments, with relatively few techniques exploiting the observations made from the cross-track sounders. This paper describes the development of the Precipitation Retrieval and Profiling Scheme (PRPS) to retrieve precipitation from cross-track sensors, together with its performance against surface radar data and other satellite precipitation retrievals.

Chris Kidd↗

NASA NTRS1998

Sarnoff JND Vision Model for Flat-Panel Design

This document describes adaptation of the basic Sarnoff JND Vision Model created in response to the NASA/ARPA need for a general-purpose model to predict the perceived image quality attained by flat-panel displays. The JND model predicts the perceptual ratings that humans will assign to a degraded color-image sequence relative to its nondegraded counterpart. Substantial flexibility is incorporated into this version of the model so it may be used to model displays at the sub-pixel and sub-frame level. To model a display (e.g., an LCD), the input-image data can be sampled at many times the pixel resolution and at many times the digital frame rate. The first stage of the model downsamples each sequence in time and in space to physiologically reasonable rates, but with minimum interpolative artifacts and aliasing. Luma and chroma parts of the model generate (through multi-resolution pyramid representation) a map of differences-between test and reference called the JND map, from which a summary rating predictor is derived. The latest model extensions have done well in calibration against psychophysical data and against image-rating data given a CRT-based front-end. THe software was delivered to NASA Ames and is being integrated with LCD display models at that facility,

Brill, Michael H.↗